Overview

Dataset statistics

Number of variables42
Number of observations133.892
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory42.9 MiB
Average record size in memory336.0 B

Variable types

Numeric25
Categorical11
DateTime3
Text3

Alerts

GESTOR_TP is highly imbalanced (61.6%)Imbalance
AUD_JUST is highly imbalanced (80.5%)Imbalance
TPDISEC2 is highly imbalanced (88.9%)Imbalance
PROC_SOLIC is highly skewed (γ1 = 63.01133799)Skewed
PROC_REA is highly skewed (γ1 = 70.70512858)Skewed
CGC_HOSP has 11466 (8.6%) zerosZeros
DIAR_ACOM has 128592 (96.0%) zerosZeros
VAL_SH has 5337 (4.0%) zerosZeros
VAL_SP has 5337 (4.0%) zerosZeros
VAL_TOT has 5337 (4.0%) zerosZeros
US_TOT has 5337 (4.0%) zerosZeros
DIAS_PERM has 2571 (1.9%) zerosZeros
GESTOR_CPF has 123875 (92.5%) zerosZeros
CNPJ_MANT has 76185 (56.9%) zerosZeros

Reproduction

Analysis started2024-04-29 20:01:13.203905
Analysis finished2024-04-29 20:06:02.042318
Duration4 minutes and 48.84 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

UF_ZI
Real number (ℝ)

Distinct231
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean351355.03
Minimum350000
Maximum355700
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-04-29T17:06:02.282199image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum350000
5-th percentile350000
Q1350000
median350000
Q3352470
95-th percentile355030
Maximum355700
Range5700
Interquartile range (IQR)2470

Descriptive statistics

Standard deviation1958.9768
Coefficient of variation (CV)0.0055754911
Kurtosis-0.51943059
Mean351355.03
Median Absolute Deviation (MAD)0
Skewness1.0857069
Sum4.7043627 × 1010
Variance3837590.2
MonotonicityNot monotonic
2024-04-29T17:06:02.813529image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
350000 71027
53.0%
355030 10704
 
8.0%
354980 5607
 
4.2%
350190 5095
 
3.8%
351110 3774
 
2.8%
351620 3090
 
2.3%
354990 2366
 
1.8%
352900 2000
 
1.5%
350160 1729
 
1.3%
353730 1594
 
1.2%
Other values (221) 26906
 
20.1%
ValueCountFrequency (%)
350000 71027
53.0%
350010 1515
 
1.1%
350050 34
 
< 0.1%
350100 240
 
0.2%
350110 5
 
< 0.1%
350130 25
 
< 0.1%
350160 1729
 
1.3%
350170 11
 
< 0.1%
350190 5095
 
3.8%
350210 1
 
< 0.1%
ValueCountFrequency (%)
355700 48
 
< 0.1%
355680 27
 
< 0.1%
355670 102
 
0.1%
355650 113
 
0.1%
355630 39
 
< 0.1%
355620 1
 
< 0.1%
355600 1
 
< 0.1%
355540 424
0.3%
355470 2
 
< 0.1%
355430 57
 
< 0.1%

ANO_CMPT
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
2023
33473 
2019
28396 
2022
26645 
2020
22746 
2021
22632 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters535.568
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019
2nd row2019
3rd row2019
4th row2019
5th row2019

Common Values

ValueCountFrequency (%)
2023 33473
25.0%
2019 28396
21.2%
2022 26645
19.9%
2020 22746
17.0%
2021 22632
16.9%

Length

2024-04-29T17:06:03.330832image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-29T17:06:03.767959image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
2023 33473
25.0%
2019 28396
21.2%
2022 26645
19.9%
2020 22746
17.0%
2021 22632
16.9%

Most occurring characters

ValueCountFrequency (%)
2 266033
49.7%
0 156638
29.2%
1 51028
 
9.5%
3 33473
 
6.2%
9 28396
 
5.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 535568
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 266033
49.7%
0 156638
29.2%
1 51028
 
9.5%
3 33473
 
6.2%
9 28396
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 535568
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 266033
49.7%
0 156638
29.2%
1 51028
 
9.5%
3 33473
 
6.2%
9 28396
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 535568
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 266033
49.7%
0 156638
29.2%
1 51028
 
9.5%
3 33473
 
6.2%
9 28396
 
5.3%

MES_CMPT
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5785932
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-04-29T17:06:04.191217image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.4879537
Coefficient of variation (CV)0.53019751
Kurtosis-1.2452028
Mean6.5785932
Median Absolute Deviation (MAD)3
Skewness-0.035636535
Sum880821
Variance12.165821
MonotonicityNot monotonic
2024-04-29T17:06:04.584356image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
10 11818
8.8%
12 11759
8.8%
11 11656
8.7%
3 11552
8.6%
9 11324
8.5%
8 11311
8.4%
1 11252
8.4%
2 10889
8.1%
7 10813
8.1%
5 10704
8.0%
Other values (2) 20814
15.5%
ValueCountFrequency (%)
1 11252
8.4%
2 10889
8.1%
3 11552
8.6%
4 10434
7.8%
5 10704
8.0%
6 10380
7.8%
7 10813
8.1%
8 11311
8.4%
9 11324
8.5%
10 11818
8.8%
ValueCountFrequency (%)
12 11759
8.8%
11 11656
8.7%
10 11818
8.8%
9 11324
8.5%
8 11311
8.4%
7 10813
8.1%
6 10380
7.8%
5 10704
8.0%
4 10434
7.8%
3 11552
8.6%

ESPEC
Real number (ℝ)

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.8864981
Minimum1
Maximum87
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-04-29T17:06:04.966347image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q15
median5
Q35
95-th percentile14
Maximum87
Range86
Interquartile range (IQR)0

Descriptive statistics

Standard deviation15.485522
Coefficient of variation (CV)1.9635486
Kurtosis21.950292
Mean7.8864981
Median Absolute Deviation (MAD)0
Skewness4.8745221
Sum1055939
Variance239.80139
MonotonicityNot monotonic
2024-04-29T17:06:05.326023image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
5 108168
80.8%
3 17688
 
13.2%
87 4903
 
3.7%
14 2198
 
1.6%
7 538
 
0.4%
2 352
 
0.3%
1 18
 
< 0.1%
4 12
 
< 0.1%
13 9
 
< 0.1%
8 5
 
< 0.1%
ValueCountFrequency (%)
1 18
 
< 0.1%
2 352
 
0.3%
3 17688
 
13.2%
4 12
 
< 0.1%
5 108168
80.8%
7 538
 
0.4%
8 5
 
< 0.1%
9 1
 
< 0.1%
13 9
 
< 0.1%
14 2198
 
1.6%
ValueCountFrequency (%)
87 4903
 
3.7%
14 2198
 
1.6%
13 9
 
< 0.1%
9 1
 
< 0.1%
8 5
 
< 0.1%
7 538
 
0.4%
5 108168
80.8%
4 12
 
< 0.1%
3 17688
 
13.2%
2 352
 
0.3%

CGC_HOSP
Real number (ℝ)

ZEROS 

Distinct347
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4470717 × 1013
Minimum0
Maximum7.2957814 × 1013
Zeros11466
Zeros (%)8.6%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-04-29T17:06:05.847973image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.63745 × 1013
median4.6392148 × 1013
Q35.07566 × 1013
95-th percentile6.019499 × 1013
Maximum7.2957814 × 1013
Range7.2957814 × 1013
Interquartile range (IQR)4.3821 × 1012

Descriptive statistics

Standard deviation1.6165245 × 1013
Coefficient of variation (CV)0.36350313
Kurtosis3.1041664
Mean4.4470717 × 1013
Median Absolute Deviation (MAD)3.125928 × 1012
Skewness-1.9667595
Sum5.9542733 × 1018
Variance2.6131515 × 1026
MonotonicityNot monotonic
2024-04-29T17:06:07.368523image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.9914773 × 101312131
 
9.1%
0 11466
 
8.6%
5.9986224 × 10135606
 
4.2%
5.4228366 × 10135474
 
4.1%
4.3464031 × 10135032
 
3.8%
5.07566 × 10134305
 
3.2%
4.8209704 × 10134035
 
3.0%
4.7078019 × 10133774
 
2.8%
5.6390123 × 10133665
 
2.7%
4.7957667 × 10133090
 
2.3%
Other values (337) 75314
56.2%
ValueCountFrequency (%)
0 11466
8.6%
3.394000019 × 10101594
 
1.2%
2.41171 × 10121
 
< 0.1%
2.927389 × 10125
 
< 0.1%
6.352252 × 1012931
 
0.7%
7.956704 × 10122
 
< 0.1%
9.161265 × 1012293
 
0.2%
9.528436 × 10121
 
< 0.1%
1.133775 × 101321
 
< 0.1%
1.3370183 × 10131
 
< 0.1%
ValueCountFrequency (%)
7.2957814 × 101314
 
< 0.1%
7.2938079 × 101327
 
< 0.1%
7.2909179 × 1013102
 
0.1%
7.2835804 × 101339
 
< 0.1%
7.279028 × 10131
 
< 0.1%
7.2747967 × 1013424
0.3%
7.2547623 × 10134
 
< 0.1%
7.2189582 × 101377
 
0.1%
7.212721 × 101311
 
< 0.1%
7.2079114 × 1013444
0.3%

N_AIH
Real number (ℝ)

Distinct98513
Distinct (%)73.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5211022 × 1012
Minimum3.5081009 × 1012
Maximum3.5231317 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-04-29T17:06:07.877516image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum3.5081009 × 1012
5-th percentile3.5191035 × 1012
Q13.5201007 × 1012
median3.5211168 × 1012
Q33.5221278 × 1012
95-th percentile3.5231212 × 1012
Maximum3.5231317 × 1012
Range1.5030801 × 1010
Interquartile range (IQR)2.0271252 × 109

Descriptive statistics

Standard deviation1.6437999 × 109
Coefficient of variation (CV)0.00046684243
Kurtosis4.3708483
Mean3.5211022 × 1012
Median Absolute Deviation (MAD)1.0142795 × 109
Skewness-0.96215135
Sum4.7144741 × 1017
Variance2.7020782 × 1018
MonotonicityNot monotonic
2024-04-29T17:06:08.405274image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.515112099 × 101260
 
< 0.1%
3.517101778 × 101260
 
< 0.1%
3.508118575 × 101260
 
< 0.1%
3.514108688 × 101260
 
< 0.1%
3.5181064 × 101259
 
< 0.1%
3.511104635 × 101259
 
< 0.1%
3.518106376 × 101259
 
< 0.1%
3.512115599 × 101258
 
< 0.1%
3.519117615 × 101251
 
< 0.1%
3.514100598 × 101251
 
< 0.1%
Other values (98503) 133315
99.6%
ValueCountFrequency (%)
3.508100889 × 101229
< 0.1%
3.508102541 × 10128
 
< 0.1%
3.508102554 × 10121
 
< 0.1%
3.508103318 × 101219
 
< 0.1%
3.50810332 × 101212
 
< 0.1%
3.508118575 × 101260
< 0.1%
3.509122723 × 10127
 
< 0.1%
3.510104219 × 101212
 
< 0.1%
3.510120617 × 101213
 
< 0.1%
3.511104635 × 101259
< 0.1%
ValueCountFrequency (%)
3.52313169 × 10121
< 0.1%
3.52313169 × 10121
< 0.1%
3.52313169 × 10121
< 0.1%
3.52313169 × 10121
< 0.1%
3.52313169 × 10121
< 0.1%
3.523131688 × 10121
< 0.1%
3.523131687 × 10121
< 0.1%
3.523131675 × 10121
< 0.1%
3.523131649 × 10121
< 0.1%
3.523131649 × 10121
< 0.1%

IDENT
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
1
97910 
5
35982 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters133.892
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 97910
73.1%
5 35982
 
26.9%

Length

2024-04-29T17:06:08.960344image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-29T17:06:09.361276image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 97910
73.1%
5 35982
 
26.9%

Most occurring characters

ValueCountFrequency (%)
1 97910
73.1%
5 35982
 
26.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 133892
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 97910
73.1%
5 35982
 
26.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 133892
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 97910
73.1%
5 35982
 
26.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 133892
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 97910
73.1%
5 35982
 
26.9%

CEP
Real number (ℝ)

Distinct36844
Distinct (%)27.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12113200
Minimum1001000
Maximum95700000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-04-29T17:06:09.825988image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1001000
5-th percentile2443000
Q18320450
median13510000
Q315054673
95-th percentile19053680
Maximum95700000
Range94699000
Interquartile range (IQR)6734223

Descriptive statistics

Standard deviation5387789.6
Coefficient of variation (CV)0.44478663
Kurtosis19.376503
Mean12113200
Median Absolute Deviation (MAD)3410000
Skewness1.185805
Sum1.6218606 × 1012
Variance2.9028277 × 1013
MonotonicityNot monotonic
2024-04-29T17:06:10.396991image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1121000 2746
 
2.1%
13990000 1786
 
1.3%
2938000 1001
 
0.7%
14240000 985
 
0.7%
1214000 654
 
0.5%
4301002 629
 
0.5%
13700000 620
 
0.5%
13920000 556
 
0.4%
17400000 545
 
0.4%
13720000 505
 
0.4%
Other values (36834) 123865
92.5%
ValueCountFrequency (%)
1001000 6
< 0.1%
1001001 1
 
< 0.1%
1003000 1
 
< 0.1%
1005020 1
 
< 0.1%
1006020 2
 
< 0.1%
1007000 1
 
< 0.1%
1007020 2
 
< 0.1%
1008000 3
< 0.1%
1009000 1
 
< 0.1%
1009999 2
 
< 0.1%
ValueCountFrequency (%)
95700000 2
< 0.1%
95115530 1
< 0.1%
91530100 1
< 0.1%
91120128 1
< 0.1%
89909971 1
< 0.1%
89900000 2
< 0.1%
89809556 2
< 0.1%
89520000 1
< 0.1%
89237382 2
< 0.1%
88902330 1
< 0.1%

MUNIC_RES
Real number (ℝ)

Distinct755
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean353307.73
Minimum120040
Maximum530010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-04-29T17:06:10.939314image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum120040
5-th percentile350430
Q1351820
median353730
Q3355030
95-th percentile355280
Maximum530010
Range409970
Interquartile range (IQR)3210

Descriptive statistics

Standard deviation4253.8078
Coefficient of variation (CV)0.012039951
Kurtosis1106.0521
Mean353307.73
Median Absolute Deviation (MAD)1300
Skewness-7.424406
Sum4.7305078 × 1010
Variance18094881
MonotonicityNot monotonic
2024-04-29T17:06:11.460967image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
355030 26620
 
19.9%
354980 4432
 
3.3%
354990 2375
 
1.8%
351620 2370
 
1.8%
354140 2364
 
1.8%
352900 2290
 
1.7%
354340 2264
 
1.7%
351880 1918
 
1.4%
350950 1883
 
1.4%
352260 1820
 
1.4%
Other values (745) 85556
63.9%
ValueCountFrequency (%)
120040 1
 
< 0.1%
130080 1
 
< 0.1%
130260 5
< 0.1%
150140 5
< 0.1%
150775 1
 
< 0.1%
172100 1
 
< 0.1%
210480 1
 
< 0.1%
220210 1
 
< 0.1%
230030 1
 
< 0.1%
230440 2
 
< 0.1%
ValueCountFrequency (%)
530010 1
< 0.1%
522185 1
< 0.1%
520870 2
< 0.1%
520640 1
< 0.1%
520140 1
< 0.1%
510792 2
< 0.1%
510779 1
< 0.1%
510760 1
< 0.1%
510510 1
< 0.1%
500830 1
< 0.1%

NASC
Date

Distinct19190
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
Minimum1970-01-01 00:00:00.018970
Maximum1970-01-01 00:00:00.020230
2024-04-29T17:06:12.117870image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:06:12.692336image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

SEXO
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
1
76490 
3
57402 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters133.892
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row3
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 76490
57.1%
3 57402
42.9%

Length

2024-04-29T17:06:13.346813image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-29T17:06:13.670940image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 76490
57.1%
3 57402
42.9%

Most occurring characters

ValueCountFrequency (%)
1 76490
57.1%
3 57402
42.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 133892
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 76490
57.1%
3 57402
42.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 133892
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 76490
57.1%
3 57402
42.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 133892
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 76490
57.1%
3 57402
42.9%

DIAR_ACOM
Real number (ℝ)

ZEROS 

Distinct35
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.19471664
Minimum0
Maximum37
Zeros128592
Zeros (%)96.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-04-29T17:06:14.060379image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum37
Range37
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.468026
Coefficient of variation (CV)7.5392942
Kurtosis189.4173
Mean0.19471664
Median Absolute Deviation (MAD)0
Skewness12.279722
Sum26071
Variance2.1551004
MonotonicityNot monotonic
2024-04-29T17:06:14.535938image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
0 128592
96.0%
1 1456
 
1.1%
2 925
 
0.7%
3 661
 
0.5%
4 412
 
0.3%
5 347
 
0.3%
6 301
 
0.2%
7 210
 
0.2%
8 164
 
0.1%
9 135
 
0.1%
Other values (25) 689
 
0.5%
ValueCountFrequency (%)
0 128592
96.0%
1 1456
 
1.1%
2 925
 
0.7%
3 661
 
0.5%
4 412
 
0.3%
5 347
 
0.3%
6 301
 
0.2%
7 210
 
0.2%
8 164
 
0.1%
9 135
 
0.1%
ValueCountFrequency (%)
37 1
 
< 0.1%
34 2
 
< 0.1%
33 3
 
< 0.1%
32 1
 
< 0.1%
30 53
< 0.1%
29 19
 
< 0.1%
28 8
 
< 0.1%
27 10
 
< 0.1%
26 10
 
< 0.1%
25 10
 
< 0.1%

QT_DIARIAS
Real number (ℝ)

Distinct59
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.788501
Minimum0
Maximum104
Zeros1183
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-04-29T17:06:15.129661image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median11
Q323
95-th percentile31
Maximum104
Range104
Interquartile range (IQR)19

Descriptive statistics

Standard deviation10.805386
Coefficient of variation (CV)0.78365196
Kurtosis-1.0309317
Mean13.788501
Median Absolute Deviation (MAD)8
Skewness0.48652273
Sum1846170
Variance116.75637
MonotonicityNot monotonic
2024-04-29T17:06:15.806078image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 11281
 
8.4%
31 10315
 
7.7%
2 10036
 
7.5%
30 8931
 
6.7%
3 8533
 
6.4%
4 6829
 
5.1%
5 5648
 
4.2%
6 5243
 
3.9%
7 4681
 
3.5%
8 4194
 
3.1%
Other values (49) 58201
43.5%
ValueCountFrequency (%)
0 1183
 
0.9%
1 11281
8.4%
2 10036
7.5%
3 8533
6.4%
4 6829
5.1%
5 5648
4.2%
6 5243
3.9%
7 4681
3.5%
8 4194
 
3.1%
9 3777
 
2.8%
ValueCountFrequency (%)
104 1
< 0.1%
101 1
< 0.1%
91 1
< 0.1%
83 1
< 0.1%
79 1
< 0.1%
70 1
< 0.1%
67 1
< 0.1%
63 1
< 0.1%
55 1
< 0.1%
51 1
< 0.1%

PROC_SOLIC
Real number (ℝ)

SKEWED 

Distinct92
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0325849 × 108
Minimum3.0106001 × 108
Maximum5.0602004 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-04-29T17:06:16.226862image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum3.0106001 × 108
5-th percentile3.0317009 × 108
Q13.0317014 × 108
median3.0317018 × 108
Q33.0317019 × 108
95-th percentile3.031702 × 108
Maximum5.0602004 × 108
Range2.0496004 × 108
Interquartile range (IQR)50

Descriptive statistics

Standard deviation1887675.3
Coefficient of variation (CV)0.0062246414
Kurtosis5372.0032
Mean3.0325849 × 108
Median Absolute Deviation (MAD)8
Skewness63.011338
Sum4.0603886 × 1013
Variance3.5633182 × 1012
MonotonicityNot monotonic
2024-04-29T17:06:16.827914image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
303170190 56242
42.0%
303170140 28286
21.1%
303170093 12408
 
9.3%
303170182 12119
 
9.1%
303170131 8675
 
6.5%
303170204 4619
 
3.4%
308020030 3231
 
2.4%
301060088 3141
 
2.3%
303170107 2214
 
1.7%
303170158 1656
 
1.2%
Other values (82) 1301
 
1.0%
ValueCountFrequency (%)
301060010 319
 
0.2%
301060070 17
 
< 0.1%
301060088 3141
2.3%
301090017 7
 
< 0.1%
301090025 2
 
< 0.1%
303010010 1
 
< 0.1%
303010037 7
 
< 0.1%
303010061 3
 
< 0.1%
303010134 1
 
< 0.1%
303010142 1
 
< 0.1%
ValueCountFrequency (%)
506020045 2
 
< 0.1%
503010014 2
 
< 0.1%
415040027 2
 
< 0.1%
415020077 1
 
< 0.1%
415010012 2
 
< 0.1%
413010015 1
 
< 0.1%
411010034 1
 
< 0.1%
411010026 6
< 0.1%
409010090 1
 
< 0.1%
408060484 1
 
< 0.1%

PROC_REA
Real number (ℝ)

SKEWED 

Distinct70
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.032505 × 108
Minimum3.0106001 × 108
Maximum5.0602004 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-04-29T17:06:17.255913image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum3.0106001 × 108
5-th percentile3.0317009 × 108
Q13.0317014 × 108
median3.0317018 × 108
Q33.0317019 × 108
95-th percentile3.031702 × 108
Maximum5.0602004 × 108
Range2.0496004 × 108
Interquartile range (IQR)50

Descriptive statistics

Standard deviation1720222.6
Coefficient of variation (CV)0.0056726125
Kurtosis7008.7909
Mean3.032505 × 108
Median Absolute Deviation (MAD)8
Skewness70.705129
Sum4.0602816 × 1013
Variance2.9591657 × 1012
MonotonicityNot monotonic
2024-04-29T17:06:17.777650image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
303170190 56242
42.0%
303170140 28280
21.1%
303170093 12408
 
9.3%
303170182 12188
 
9.1%
303170131 8707
 
6.5%
303170204 4619
 
3.4%
308020030 3200
 
2.4%
301060088 3178
 
2.4%
303170107 2213
 
1.7%
303170158 1644
 
1.2%
Other values (60) 1213
 
0.9%
ValueCountFrequency (%)
301060010 322
 
0.2%
301060070 17
 
< 0.1%
301060088 3178
2.4%
301090017 7
 
< 0.1%
301090025 2
 
< 0.1%
303010037 5
 
< 0.1%
303010061 3
 
< 0.1%
303010223 1
 
< 0.1%
303030020 5
 
< 0.1%
303030038 2
 
< 0.1%
ValueCountFrequency (%)
506020045 2
 
< 0.1%
503010014 2
 
< 0.1%
415020077 1
 
< 0.1%
415010012 2
 
< 0.1%
411010034 1
 
< 0.1%
411010026 6
 
< 0.1%
409010090 1
 
< 0.1%
407040161 1
 
< 0.1%
310010047 8
 
< 0.1%
308040015 85
0.1%

VAL_SH
Real number (ℝ)

ZEROS 

Distinct5094
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean749.13277
Minimum0
Maximum54389.39
Zeros5337
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-04-29T17:06:18.330643image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile33.34
Q1149.34
median580.03
Q31231.88
95-th percentile1957.6
Maximum54389.39
Range54389.39
Interquartile range (IQR)1082.54

Descriptive statistics

Standard deviation690.74615
Coefficient of variation (CV)0.92206106
Kurtosis315.1146
Mean749.13277
Median Absolute Deviation (MAD)469.7
Skewness5.6910887
Sum1.0030288 × 108
Variance477130.25
MonotonicityNot monotonic
2024-04-29T17:06:18.783419image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49.78 7450
 
5.6%
0 5337
 
4.0%
99.56 3699
 
2.8%
149.34 2946
 
2.2%
2247.61 2268
 
1.7%
199.12 2211
 
1.7%
1493.4 2198
 
1.6%
33.34 2045
 
1.5%
1909.42 1892
 
1.4%
248.9 1785
 
1.3%
Other values (5084) 102061
76.2%
ValueCountFrequency (%)
0 5337
4.0%
17.12 5
 
< 0.1%
20.03 1
 
< 0.1%
30.47 14
 
< 0.1%
33.34 2045
 
1.5%
33.95 16
 
< 0.1%
34.24 2
 
< 0.1%
34.34 38
 
< 0.1%
34.6 1
 
< 0.1%
35.34 1
 
< 0.1%
ValueCountFrequency (%)
54389.39 1
< 0.1%
27198.94 1
< 0.1%
24905.02 1
< 0.1%
20395.64 1
< 0.1%
19272.59 1
< 0.1%
17137.81 1
< 0.1%
15480.82 1
< 0.1%
14337.44 1
< 0.1%
13880.33 1
< 0.1%
11424.67 1
< 0.1%

VAL_SP
Real number (ℝ)

ZEROS 

Distinct894
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean102.98244
Minimum0
Maximum8846.62
Zeros5337
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-04-29T17:06:19.286753image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.22
Q123.88
median77.87
Q3168.23
95-th percentile267.19
Maximum8846.62
Range8846.62
Interquartile range (IQR)144.35

Descriptive statistics

Standard deviation96.494602
Coefficient of variation (CV)0.93700057
Kurtosis571.50011
Mean102.98244
Median Absolute Deviation (MAD)63.43
Skewness8.3250145
Sum13788524
Variance9311.2081
MonotonicityNot monotonic
2024-04-29T17:06:19.749735image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.22 9033
 
6.7%
0 5337
 
4.0%
14.44 4578
 
3.4%
21.66 3709
 
2.8%
10.88 3168
 
2.4%
28.88 2819
 
2.1%
216.6 2740
 
2.0%
26.62 2412
 
1.8%
36.1 2309
 
1.7%
306.78 2308
 
1.7%
Other values (884) 95479
71.3%
ValueCountFrequency (%)
0 5337
4.0%
3.3 97
 
0.1%
4.64 16
 
< 0.1%
4.86 5
 
< 0.1%
5 1
 
< 0.1%
5.09 139
 
0.1%
5.73 1
 
< 0.1%
5.97 372
 
0.3%
6.23 34
 
< 0.1%
6.56 2
 
< 0.1%
ValueCountFrequency (%)
8846.62 1
< 0.1%
4173.37 1
< 0.1%
4021.27 1
< 0.1%
3104.98 1
< 0.1%
2901.01 1
< 0.1%
2533.85 1
< 0.1%
2456.62 1
< 0.1%
2281.61 1
< 0.1%
1837.85 1
< 0.1%
1827.52 1
< 0.1%

VAL_TOT
Real number (ℝ)

ZEROS 

Distinct5261
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean852.11521
Minimum0
Maximum63236.01
Zeros5337
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-04-29T17:06:20.207652image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile44.22
Q1171
median659.19
Q31400.79
95-th percentile2224.79
Maximum63236.01
Range63236.01
Interquartile range (IQR)1229.79

Descriptive statistics

Standard deviation786.53618
Coefficient of variation (CV)0.92303972
Kurtosis341.4698
Mean852.11521
Median Absolute Deviation (MAD)530.42
Skewness5.9768721
Sum1.1409141 × 108
Variance618639.16
MonotonicityNot monotonic
2024-04-29T17:06:20.788846image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
57 7417
 
5.5%
0 5337
 
4.0%
114 3695
 
2.8%
171 2945
 
2.2%
2554.39 2268
 
1.7%
228 2210
 
1.7%
44.22 2045
 
1.5%
1710 1976
 
1.5%
2170.03 1892
 
1.4%
285 1785
 
1.3%
Other values (5251) 102322
76.4%
ValueCountFrequency (%)
0 5337
4.0%
21.98 5
 
< 0.1%
25.03 1
 
< 0.1%
38.59 16
 
< 0.1%
39.88 96
 
0.1%
40.38 14
 
< 0.1%
42.37 139
 
0.1%
43.04 1
 
< 0.1%
43.96 2
 
< 0.1%
44.22 2045
 
1.5%
ValueCountFrequency (%)
63236.01 1
< 0.1%
31372.31 1
< 0.1%
28926.29 1
< 0.1%
23296.65 1
< 0.1%
21554.2 1
< 0.1%
20242.79 1
< 0.1%
18014.67 1
< 0.1%
16336.95 1
< 0.1%
16074.06 1
< 0.1%
13252.19 1
< 0.1%

US_TOT
Real number (ℝ)

ZEROS 

Distinct18865
Distinct (%)14.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean175.41708
Minimum0
Maximum13146.77
Zeros5337
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-04-29T17:06:21.280766image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8.75
Q138.08
median134.325
Q3286.9975
95-th percentile468.307
Maximum13146.77
Range13146.77
Interquartile range (IQR)248.9175

Descriptive statistics

Standard deviation163.52533
Coefficient of variation (CV)0.93220871
Kurtosis333.15673
Mean175.41708
Median Absolute Deviation (MAD)107.585
Skewness5.808658
Sum23486943
Variance26740.533
MonotonicityNot monotonic
2024-04-29T17:06:21.909838image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5337
 
4.0%
11.4 358
 
0.3%
14.46 299
 
0.2%
10.42 298
 
0.2%
10.51 247
 
0.2%
10.98 246
 
0.2%
10.1 244
 
0.2%
11.19 235
 
0.2%
10.55 227
 
0.2%
10.89 219
 
0.2%
Other values (18855) 126182
94.2%
ValueCountFrequency (%)
0 5337
4.0%
3.86 1
 
< 0.1%
3.94 1
 
< 0.1%
3.98 1
 
< 0.1%
4.2 1
 
< 0.1%
4.65 1
 
< 0.1%
6.65 1
 
< 0.1%
6.84 1
 
< 0.1%
6.87 1
 
< 0.1%
7 4
 
< 0.1%
ValueCountFrequency (%)
13146.77 1
< 0.1%
6363.55 1
< 0.1%
5638.65 1
< 0.1%
4506.12 1
< 0.1%
4182.39 1
< 0.1%
3991.51 1
< 0.1%
3464.35 1
< 0.1%
3159.95 1
< 0.1%
3157.96 1
< 0.1%
2558.33 1
< 0.1%
Distinct1996
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
Minimum1970-01-01 00:00:00.020080
Maximum1970-01-01 00:00:00.020231
2024-04-29T17:06:22.336249image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:06:22.930641image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct1914
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
Minimum1970-01-01 00:00:00.020180
Maximum1970-01-01 00:00:00.020231
2024-04-29T17:06:23.440987image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:06:23.906071image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

DIAG_PRINC
Categorical

Distinct29
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
F192
52987 
F312
22258 
F322
9238 
F323
5497 
F195
 
4785
Other values (24)
39127 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters535.568
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF192
2nd rowF192
3rd rowF312
4th rowF312
5th rowF312

Common Values

ValueCountFrequency (%)
F192 52987
39.6%
F312 22258
16.6%
F322 9238
 
6.9%
F323 5497
 
4.1%
F195 4785
 
3.6%
F190 4575
 
3.4%
F329 4410
 
3.3%
F199 3484
 
2.6%
F311 2942
 
2.2%
F319 2814
 
2.1%
Other values (19) 20902
 
15.6%

Length

2024-04-29T17:06:24.349368image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
f192 52987
39.6%
f312 22258
16.6%
f322 9238
 
6.9%
f323 5497
 
4.1%
f195 4785
 
3.6%
f190 4575
 
3.4%
f329 4410
 
3.3%
f199 3484
 
2.6%
f311 2942
 
2.2%
f319 2814
 
2.1%
Other values (19) 20902
 
15.6%

Most occurring characters

ValueCountFrequency (%)
F 133892
25.0%
1 115298
21.5%
2 110258
20.6%
9 80411
15.0%
3 70802
13.2%
0 8784
 
1.6%
5 6188
 
1.2%
3270
 
0.6%
8 2747
 
0.5%
6 1584
 
0.3%
Other values (2) 2334
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 535568
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
F 133892
25.0%
1 115298
21.5%
2 110258
20.6%
9 80411
15.0%
3 70802
13.2%
0 8784
 
1.6%
5 6188
 
1.2%
3270
 
0.6%
8 2747
 
0.5%
6 1584
 
0.3%
Other values (2) 2334
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 535568
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
F 133892
25.0%
1 115298
21.5%
2 110258
20.6%
9 80411
15.0%
3 70802
13.2%
0 8784
 
1.6%
5 6188
 
1.2%
3270
 
0.6%
8 2747
 
0.5%
6 1584
 
0.3%
Other values (2) 2334
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 535568
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
F 133892
25.0%
1 115298
21.5%
2 110258
20.6%
9 80411
15.0%
3 70802
13.2%
0 8784
 
1.6%
5 6188
 
1.2%
3270
 
0.6%
8 2747
 
0.5%
6 1584
 
0.3%
Other values (2) 2334
 
0.4%

COBRANCA
Real number (ℝ)

Distinct22
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.674738
Minimum11
Maximum63
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-04-29T17:06:24.739962image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile12
Q112
median14
Q321
95-th percentile31
Maximum63
Range52
Interquartile range (IQR)9

Descriptive statistics

Standard deviation8.3349064
Coefficient of variation (CV)0.47157171
Kurtosis6.1810786
Mean17.674738
Median Absolute Deviation (MAD)2
Skewness2.3008944
Sum2366506
Variance69.470665
MonotonicityNot monotonic
2024-04-29T17:06:25.214965image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
12 62484
46.7%
21 34536
25.8%
14 9520
 
7.1%
31 8372
 
6.3%
16 4949
 
3.7%
51 4447
 
3.3%
15 3575
 
2.7%
23 3316
 
2.5%
18 803
 
0.6%
26 665
 
0.5%
Other values (12) 1225
 
0.9%
ValueCountFrequency (%)
11 102
 
0.1%
12 62484
46.7%
14 9520
 
7.1%
15 3575
 
2.7%
16 4949
 
3.7%
18 803
 
0.6%
19 432
 
0.3%
21 34536
25.8%
22 53
 
< 0.1%
23 3316
 
2.5%
ValueCountFrequency (%)
63 1
 
< 0.1%
62 3
 
< 0.1%
61 9
 
< 0.1%
51 4447
3.3%
43 28
 
< 0.1%
42 34
 
< 0.1%
41 103
 
0.1%
31 8372
6.3%
28 447
 
0.3%
27 11
 
< 0.1%

NAT_JUR
Real number (ℝ)

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2690.4383
Minimum1023
Maximum3999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-04-29T17:06:25.652203image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1023
5-th percentile1023
Q11031
median3069
Q33999
95-th percentile3999
Maximum3999
Range2976
Interquartile range (IQR)2968

Descriptive statistics

Standard deviation1356.9975
Coefficient of variation (CV)0.50437784
Kurtosis-1.7904834
Mean2690.4383
Median Absolute Deviation (MAD)930
Skewness-0.27241808
Sum3.6022817 × 108
Variance1841442.1
MonotonicityNot monotonic
2024-04-29T17:06:26.054568image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
3999 60507
45.2%
1023 31402
23.5%
3069 19543
 
14.6%
1244 11042
 
8.2%
1031 9162
 
6.8%
1120 918
 
0.7%
1112 784
 
0.6%
2011 281
 
0.2%
2240 192
 
0.1%
1155 20
 
< 0.1%
Other values (3) 41
 
< 0.1%
ValueCountFrequency (%)
1023 31402
23.5%
1031 9162
 
6.8%
1112 784
 
0.6%
1120 918
 
0.7%
1155 20
 
< 0.1%
1210 15
 
< 0.1%
1244 11042
 
8.2%
1279 17
 
< 0.1%
2011 281
 
0.2%
2062 9
 
< 0.1%
ValueCountFrequency (%)
3999 60507
45.2%
3069 19543
 
14.6%
2240 192
 
0.1%
2062 9
 
< 0.1%
2011 281
 
0.2%
1279 17
 
< 0.1%
1244 11042
 
8.2%
1210 15
 
< 0.1%
1155 20
 
< 0.1%
1120 918
 
0.7%

GESTAO
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
2
71027 
1
62865 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters133.892
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 71027
53.0%
1 62865
47.0%

Length

2024-04-29T17:06:26.545899image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-29T17:06:26.837017image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
2 71027
53.0%
1 62865
47.0%

Most occurring characters

ValueCountFrequency (%)
2 71027
53.0%
1 62865
47.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 133892
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 71027
53.0%
1 62865
47.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 133892
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 71027
53.0%
1 62865
47.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 133892
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 71027
53.0%
1 62865
47.0%

MUNIC_MOV
Real number (ℝ)

Distinct267
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean353069.16
Minimum350010
Maximum355710
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-04-29T17:06:27.192218image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum350010
5-th percentile350190
Q1351620
median352710
Q3354980
95-th percentile355030
Maximum355710
Range5700
Interquartile range (IQR)3360

Descriptive statistics

Standard deviation1702.3451
Coefficient of variation (CV)0.0048215627
Kurtosis-1.4103346
Mean353069.16
Median Absolute Deviation (MAD)1630
Skewness-0.14957032
Sum4.7273136 × 1010
Variance2897978.8
MonotonicityNot monotonic
2024-04-29T17:06:27.731004image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
355030 23982
17.9%
352260 12199
 
9.1%
351518 5682
 
4.2%
354980 5644
 
4.2%
354140 5482
 
4.1%
350190 5095
 
3.8%
352530 4387
 
3.3%
351670 4036
 
3.0%
351110 3844
 
2.9%
354390 3665
 
2.7%
Other values (257) 59876
44.7%
ValueCountFrequency (%)
350010 1515
 
1.1%
350050 34
 
< 0.1%
350100 240
 
0.2%
350110 5
 
< 0.1%
350130 25
 
< 0.1%
350160 1729
 
1.3%
350170 25
 
< 0.1%
350190 5095
3.8%
350210 2
 
< 0.1%
350220 7
 
< 0.1%
ValueCountFrequency (%)
355710 14
 
< 0.1%
355700 48
 
< 0.1%
355680 27
 
< 0.1%
355670 102
 
0.1%
355650 113
 
0.1%
355630 39
 
< 0.1%
355620 1
 
< 0.1%
355600 1
 
< 0.1%
355540 424
0.3%
355500 4
 
< 0.1%

IDADE
Real number (ℝ)

Distinct100
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.113898
Minimum0
Maximum99
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-04-29T17:06:28.225840image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile18
Q127
median36
Q346
95-th percentile62
Maximum99
Range99
Interquartile range (IQR)19

Descriptive statistics

Standard deviation13.273623
Coefficient of variation (CV)0.35764561
Kurtosis-0.031258942
Mean37.113898
Median Absolute Deviation (MAD)9
Skewness0.54210723
Sum4969254
Variance176.18906
MonotonicityNot monotonic
2024-04-29T17:06:28.710697image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36 4038
 
3.0%
33 4019
 
3.0%
37 3923
 
2.9%
34 3895
 
2.9%
35 3852
 
2.9%
32 3827
 
2.9%
38 3809
 
2.8%
39 3789
 
2.8%
31 3718
 
2.8%
40 3713
 
2.8%
Other values (90) 95309
71.2%
ValueCountFrequency (%)
0 2
 
< 0.1%
1 6
 
< 0.1%
2 1
 
< 0.1%
3 2
 
< 0.1%
4 2
 
< 0.1%
5 1
 
< 0.1%
6 2
 
< 0.1%
7 6
 
< 0.1%
8 9
< 0.1%
9 18
< 0.1%
ValueCountFrequency (%)
99 5
< 0.1%
98 12
< 0.1%
97 8
< 0.1%
96 5
< 0.1%
95 7
< 0.1%
94 2
 
< 0.1%
93 2
 
< 0.1%
92 1
 
< 0.1%
91 3
 
< 0.1%
90 7
< 0.1%

DIAS_PERM
Real number (ℝ)

ZEROS 

Distinct62
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.13062
Minimum0
Maximum120
Zeros2571
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-04-29T17:06:29.167605image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median12
Q324
95-th percentile31
Maximum120
Range120
Interquartile range (IQR)20

Descriptive statistics

Standard deviation10.938742
Coefficient of variation (CV)0.77411617
Kurtosis-0.92871915
Mean14.13062
Median Absolute Deviation (MAD)9
Skewness0.47129866
Sum1891977
Variance119.65607
MonotonicityNot monotonic
2024-04-29T17:06:29.699386image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 10201
 
7.6%
31 9737
 
7.3%
30 9610
 
7.2%
1 8980
 
6.7%
3 8112
 
6.1%
4 6624
 
4.9%
5 5540
 
4.1%
6 4997
 
3.7%
7 4813
 
3.6%
8 4072
 
3.0%
Other values (52) 61206
45.7%
ValueCountFrequency (%)
0 2571
 
1.9%
1 8980
6.7%
2 10201
7.6%
3 8112
6.1%
4 6624
4.9%
5 5540
4.1%
6 4997
3.7%
7 4813
3.6%
8 4072
 
3.0%
9 3657
 
2.7%
ValueCountFrequency (%)
120 1
< 0.1%
104 1
< 0.1%
101 1
< 0.1%
98 1
< 0.1%
91 1
< 0.1%
79 1
< 0.1%
77 1
< 0.1%
70 1
< 0.1%
67 1
< 0.1%
63 1
< 0.1%

CAR_INT
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
2
95022 
1
38870 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters133.892
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 95022
71.0%
1 38870
29.0%

Length

2024-04-29T17:06:30.268228image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-29T17:06:30.651719image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
2 95022
71.0%
1 38870
29.0%

Most occurring characters

ValueCountFrequency (%)
2 95022
71.0%
1 38870
29.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 133892
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 95022
71.0%
1 38870
29.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 133892
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 95022
71.0%
1 38870
29.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 133892
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 95022
71.0%
1 38870
29.0%

GESTOR_TP
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
0
123875 
1
 
10017

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters133.892
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 123875
92.5%
1 10017
 
7.5%

Length

2024-04-29T17:06:31.022674image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-29T17:06:31.372958image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 123875
92.5%
1 10017
 
7.5%

Most occurring characters

ValueCountFrequency (%)
0 123875
92.5%
1 10017
 
7.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 133892
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 123875
92.5%
1 10017
 
7.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 133892
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 123875
92.5%
1 10017
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 133892
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 123875
92.5%
1 10017
 
7.5%

GESTOR_CPF
Real number (ℝ)

ZEROS 

Distinct225
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2544003 × 109
Minimum0
Maximum9.9238048 × 1010
Zeros123875
Zeros (%)92.5%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-04-29T17:06:31.789134image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile8.6031398 × 109
Maximum9.9238048 × 1010
Range9.9238048 × 1010
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.2405471 × 109
Coefficient of variation (CV)5.7721186
Kurtosis106.14321
Mean1.2544003 × 109
Median Absolute Deviation (MAD)0
Skewness9.5754224
Sum1.6795416 × 1014
Variance5.2425523 × 1019
MonotonicityNot monotonic
2024-04-29T17:06:32.339486image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 123875
92.5%
1.202259413 × 10103211
 
2.4%
4790603806 532
 
0.4%
3703352876 515
 
0.4%
2.81765398 × 1010383
 
0.3%
5151489895 349
 
0.3%
1.026310084 × 1010302
 
0.2%
7377224878 297
 
0.2%
1.13033698 × 1010292
 
0.2%
201317800 236
 
0.2%
Other values (215) 3900
 
2.9%
ValueCountFrequency (%)
0 123875
92.5%
197060811 40
 
< 0.1%
201317800 236
 
0.2%
251334899 3
 
< 0.1%
547715870 1
 
< 0.1%
740087819 12
 
< 0.1%
749763701 110
 
0.1%
756467810 1
 
< 0.1%
769404898 2
 
< 0.1%
811070727 8
 
< 0.1%
ValueCountFrequency (%)
9.923804772 × 10102
 
< 0.1%
9.831337182 × 10103
 
< 0.1%
9.605342987 × 10102
 
< 0.1%
9.322726482 × 1010204
0.2%
9.310346885 × 10105
 
< 0.1%
9.297053289 × 101041
 
< 0.1%
9.285270086 × 10109
 
< 0.1%
9.24896728 × 10101
 
< 0.1%
9.222793782 × 1010144
0.1%
9.094919672 × 101014
 
< 0.1%

CNES
Real number (ℝ)

Distinct418
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2466272.3
Minimum8028
Maximum9773657
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-04-29T17:06:32.965017image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum8028
5-th percentile2058782
Q12080028
median2084317
Q32097648
95-th percentile5586348
Maximum9773657
Range9765629
Interquartile range (IQR)17620

Descriptive statistics

Standard deviation1133395.9
Coefficient of variation (CV)0.45955831
Kurtosis14.099795
Mean2466272.3
Median Absolute Deviation (MAD)5992
Skewness3.4514365
Sum3.3021413 × 1011
Variance1.2845863 × 1012
MonotonicityNot monotonic
2024-04-29T17:06:33.411285image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2085143 12131
 
9.1%
2097648 5606
 
4.2%
2084384 5474
 
4.1%
2084317 5032
 
3.8%
2790653 4305
 
3.2%
2745356 4035
 
3.0%
2058626 3774
 
2.8%
2083159 3665
 
2.7%
2080117 3090
 
2.3%
2058782 2990
 
2.2%
Other values (408) 83790
62.6%
ValueCountFrequency (%)
8028 78
 
0.1%
8036 1
 
< 0.1%
8052 177
 
0.1%
8087 3
 
< 0.1%
8494 8
 
< 0.1%
8923 1023
0.8%
9628 39
 
< 0.1%
24228 1
 
< 0.1%
26417 4
 
< 0.1%
102075 24
 
< 0.1%
ValueCountFrequency (%)
9773657 567
0.4%
9680500 1
 
< 0.1%
9677011 2
 
< 0.1%
9639659 21
 
< 0.1%
9556095 1
 
< 0.1%
9465464 126
 
0.1%
9425802 2
 
< 0.1%
9377255 47
 
< 0.1%
9267263 16
 
< 0.1%
7947984 79
 
0.1%

CNPJ_MANT
Real number (ℝ)

ZEROS 

Distinct74
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0537433 × 1013
Minimum0
Maximum6.8020916 × 1013
Zeros76185
Zeros (%)56.9%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-04-29T17:06:33.893787image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34.63745 × 1013
95-th percentile4.6523247 × 1013
Maximum6.8020916 × 1013
Range6.8020916 × 1013
Interquartile range (IQR)4.63745 × 1013

Descriptive statistics

Standard deviation2.375279 × 1013
Coefficient of variation (CV)1.1565608
Kurtosis-1.8136469
Mean2.0537433 × 1013
Median Absolute Deviation (MAD)0
Skewness0.32460437
Sum2.7497979 × 1018
Variance5.6419503 × 1026
MonotonicityNot monotonic
2024-04-29T17:06:34.404261image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 76185
56.9%
4.63745 × 101331402
23.5%
4.639213 × 10139198
 
6.9%
6.1956496 × 10132323
 
1.7%
4.6319 × 10131491
 
1.1%
6.044804 × 10131411
 
1.1%
4.6523015 × 10131287
 
1.0%
4.6523247 × 10131233
 
0.9%
4.6522942 × 10131047
 
0.8%
4.7018676 × 1013874
 
0.7%
Other values (64) 7441
 
5.6%
ValueCountFrequency (%)
0 76185
56.9%
4.3465459 × 10132
 
< 0.1%
4.3976166 × 101311
 
< 0.1%
4.4440121 × 10135
 
< 0.1%
4.4892693 × 10134
 
< 0.1%
4.5176005 × 1013221
 
0.2%
4.5192564 × 10134
 
< 0.1%
4.5196698 × 10131
 
< 0.1%
4.5276128 × 101316
 
< 0.1%
4.5279643 × 10135
 
< 0.1%
ValueCountFrequency (%)
6.8020916 × 101339
 
< 0.1%
6.7995027 × 10139
 
< 0.1%
6.1986402 × 101319
 
< 0.1%
6.1956496 × 10132323
1.7%
6.0992427 × 101385
 
0.1%
6.0499365 × 1013357
 
0.3%
6.044804 × 10131411
1.1%
6.019499 × 1013163
 
0.1%
5.9307595 × 1013276
 
0.2%
5.8200015 × 1013213
 
0.2%

REGCT
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
0
82578 
7102
51033 
7106
 
281

Length

Max length4
Median length1
Mean length2.1497476
Min length1

Characters and Unicode

Total characters287.834
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row7102
2nd row7102
3rd row7102
4th row7102
5th row7102

Common Values

ValueCountFrequency (%)
0 82578
61.7%
7102 51033
38.1%
7106 281
 
0.2%

Length

2024-04-29T17:06:34.881248image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-29T17:06:35.346279image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 82578
61.7%
7102 51033
38.1%
7106 281
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 133892
46.5%
7 51314
 
17.8%
1 51314
 
17.8%
2 51033
 
17.7%
6 281
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 287834
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 133892
46.5%
7 51314
 
17.8%
1 51314
 
17.8%
2 51033
 
17.7%
6 281
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 287834
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 133892
46.5%
7 51314
 
17.8%
1 51314
 
17.8%
2 51033
 
17.7%
6 281
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 287834
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 133892
46.5%
7 51314
 
17.8%
1 51314
 
17.8%
2 51033
 
17.7%
6 281
 
0.1%

RACA_COR
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.797979
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-04-29T17:06:35.620813image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile99
Maximum99
Range98
Interquartile range (IQR)2

Descriptive statistics

Standard deviation26.803908
Coefficient of variation (CV)2.7356568
Kurtosis7.15632
Mean9.797979
Median Absolute Deviation (MAD)0
Skewness3.0235831
Sum1311871
Variance718.44947
MonotonicityNot monotonic
2024-04-29T17:06:35.961826image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 70681
52.8%
3 39127
29.2%
2 12428
 
9.3%
99 11077
 
8.3%
4 565
 
0.4%
5 14
 
< 0.1%
ValueCountFrequency (%)
1 70681
52.8%
2 12428
 
9.3%
3 39127
29.2%
4 565
 
0.4%
5 14
 
< 0.1%
99 11077
 
8.3%
ValueCountFrequency (%)
99 11077
 
8.3%
5 14
 
< 0.1%
4 565
 
0.4%
3 39127
29.2%
2 12428
 
9.3%
1 70681
52.8%

SEQUENCIA
Real number (ℝ)

Distinct53823
Distinct (%)40.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25270.539
Minimum1
Maximum134774
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-04-29T17:06:36.414821image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile64
Q1462
median15160.5
Q336541.25
95-th percentile93237.45
Maximum134774
Range134773
Interquartile range (IQR)36079.25

Descriptive statistics

Standard deviation30333.921
Coefficient of variation (CV)1.200367
Kurtosis0.4726413
Mean25270.539
Median Absolute Deviation (MAD)14787.5
Skewness1.2474711
Sum3.383523 × 109
Variance9.2014676 × 108
MonotonicityNot monotonic
2024-04-29T17:06:36.932992image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88 167
 
0.1%
99 162
 
0.1%
80 162
 
0.1%
90 162
 
0.1%
129 161
 
0.1%
79 161
 
0.1%
51 160
 
0.1%
59 160
 
0.1%
85 160
 
0.1%
107 159
 
0.1%
Other values (53813) 132278
98.8%
ValueCountFrequency (%)
1 34
< 0.1%
2 26
< 0.1%
3 25
< 0.1%
4 33
< 0.1%
5 32
< 0.1%
6 38
< 0.1%
7 37
< 0.1%
8 44
< 0.1%
9 59
< 0.1%
10 57
< 0.1%
ValueCountFrequency (%)
134774 1
< 0.1%
134386 1
< 0.1%
134385 1
< 0.1%
134382 1
< 0.1%
134380 1
< 0.1%
134379 1
< 0.1%
134378 1
< 0.1%
134377 1
< 0.1%
134375 1
< 0.1%
134371 1
< 0.1%
Distinct4848
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
2024-04-29T17:06:37.666233image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length21
Median length21
Mean length21
Min length21

Characters and Unicode

Total characters2.811.732
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1.309 ?
Unique (%)1.0%

Sample

1st rowHE35000001N201901.DTS
2nd rowHE35000001N201901.DTS
3rd rowHE35000001N201901.DTS
4th rowHE35000001N201901.DTS
5th rowHE35000001N201901.DTS
ValueCountFrequency (%)
he35000001n202308.dts 1633
 
1.2%
he35000001n202312.dts 1576
 
1.2%
he35000001n202310.dts 1550
 
1.2%
he35000001n202311.dts 1489
 
1.1%
he35000001n202306.dts 1488
 
1.1%
he35000001n202309.dts 1479
 
1.1%
he35000001n201910.dts 1455
 
1.1%
he35000001n202307.dts 1443
 
1.1%
he35000001n202305.dts 1443
 
1.1%
he35000001n202001.dts 1426
 
1.1%
Other values (4838) 118910
88.8%
2024-04-29T17:06:38.807699image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 781619
27.8%
2 304078
 
10.8%
1 273218
 
9.7%
3 198184
 
7.0%
5 167085
 
5.9%
H 133892
 
4.8%
D 133892
 
4.8%
S 133892
 
4.8%
T 133892
 
4.8%
. 133892
 
4.8%
Other values (8) 418088
14.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2811732
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 781619
27.8%
2 304078
 
10.8%
1 273218
 
9.7%
3 198184
 
7.0%
5 167085
 
5.9%
H 133892
 
4.8%
D 133892
 
4.8%
S 133892
 
4.8%
T 133892
 
4.8%
. 133892
 
4.8%
Other values (8) 418088
14.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2811732
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 781619
27.8%
2 304078
 
10.8%
1 273218
 
9.7%
3 198184
 
7.0%
5 167085
 
5.9%
H 133892
 
4.8%
D 133892
 
4.8%
S 133892
 
4.8%
T 133892
 
4.8%
. 133892
 
4.8%
Other values (8) 418088
14.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2811732
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 781619
27.8%
2 304078
 
10.8%
1 273218
 
9.7%
3 198184
 
7.0%
5 167085
 
5.9%
H 133892
 
4.8%
D 133892
 
4.8%
S 133892
 
4.8%
T 133892
 
4.8%
. 133892
 
4.8%
Other values (8) 418088
14.9%

AUD_JUST
Categorical

IMBALANCE 

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
None
88322 
nan
45435 
.
 
43
1.0
 
26
1
 
25
Other values (23)
 
41

Length

Max length50
Median length4
Mean length3.6979132
Min length3

Characters and Unicode

Total characters495.121
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)< 0.1%

Sample

1st rowNone
2nd rowNone
3rd rowNone
4th rowNone
5th rowNone

Common Values

ValueCountFrequency (%)
None 88322
66.0%
nan 45435
33.9%
. 43
 
< 0.1%
1.0 26
 
< 0.1%
1 25
 
< 0.1%
SAS97 15
 
< 0.1%
SITUA‡aO DE RUA 4
 
< 0.1%
NaO HOUVE TEMPO HABIL PARA CONFEC‡aO DO CNS. 2
 
< 0.1%
PACIENTE SEM IDENTIFICA€AO 1
 
< 0.1%
PACIENTE TRAZIFO PELA GCM ENTROU PELA EMERGENCIA E 1
 
< 0.1%
Other values (18) 18
 
< 0.1%

Length

2024-04-29T17:06:39.343763image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
none 88322
65.9%
nan 45435
33.9%
44
 
< 0.1%
1.0 26
 
< 0.1%
1 25
 
< 0.1%
sas97 15
 
< 0.1%
de 7
 
< 0.1%
rua 6
 
< 0.1%
sem 6
 
< 0.1%
situa‡ao 5
 
< 0.1%
Other values (56) 76
 
0.1%

Most occurring characters

ValueCountFrequency (%)
n 179192
36.2%
N 88351
17.8%
e 88322
17.8%
o 88322
17.8%
a 45445
 
9.2%
4784
 
1.0%
. 71
 
< 0.1%
A 71
 
< 0.1%
S 66
 
< 0.1%
E 63
 
< 0.1%
Other values (27) 434
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 495121
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 179192
36.2%
N 88351
17.8%
e 88322
17.8%
o 88322
17.8%
a 45445
 
9.2%
4784
 
1.0%
. 71
 
< 0.1%
A 71
 
< 0.1%
S 66
 
< 0.1%
E 63
 
< 0.1%
Other values (27) 434
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 495121
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 179192
36.2%
N 88351
17.8%
e 88322
17.8%
o 88322
17.8%
a 45445
 
9.2%
4784
 
1.0%
. 71
 
< 0.1%
A 71
 
< 0.1%
S 66
 
< 0.1%
E 63
 
< 0.1%
Other values (27) 434
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 495121
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 179192
36.2%
N 88351
17.8%
e 88322
17.8%
o 88322
17.8%
a 45445
 
9.2%
4784
 
1.0%
. 71
 
< 0.1%
A 71
 
< 0.1%
S 66
 
< 0.1%
E 63
 
< 0.1%
Other values (27) 434
 
0.1%
Distinct1331
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
2024-04-29T17:06:40.443824image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length7
Median length7
Mean length6.3158964
Min length4

Characters and Unicode

Total characters845.648
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique608 ?
Unique (%)0.5%

Sample

1st rowno_info
2nd rowF209
3rd rowF200
4th rowF192
5th rowno_info
ValueCountFrequency (%)
no_info 103360
77.2%
f102 2437
 
1.8%
f29 2211
 
1.7%
f192 1315
 
1.0%
f603 1159
 
0.9%
f200 1086
 
0.8%
f918 952
 
0.7%
f172 767
 
0.6%
f411 732
 
0.5%
i10 678
 
0.5%
Other values (1321) 19195
 
14.3%
2024-04-29T17:06:42.101957image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 206720
24.4%
o 206720
24.4%
_ 103360
12.2%
i 103360
12.2%
f 103360
12.2%
F 24208
 
2.9%
1 16661
 
2.0%
2 16152
 
1.9%
0 15504
 
1.8%
9 11178
 
1.3%
Other values (30) 38425
 
4.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 845648
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 206720
24.4%
o 206720
24.4%
_ 103360
12.2%
i 103360
12.2%
f 103360
12.2%
F 24208
 
2.9%
1 16661
 
2.0%
2 16152
 
1.9%
0 15504
 
1.8%
9 11178
 
1.3%
Other values (30) 38425
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 845648
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 206720
24.4%
o 206720
24.4%
_ 103360
12.2%
i 103360
12.2%
f 103360
12.2%
F 24208
 
2.9%
1 16661
 
2.0%
2 16152
 
1.9%
0 15504
 
1.8%
9 11178
 
1.3%
Other values (30) 38425
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 845648
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 206720
24.4%
o 206720
24.4%
_ 103360
12.2%
i 103360
12.2%
f 103360
12.2%
F 24208
 
2.9%
1 16661
 
2.0%
2 16152
 
1.9%
0 15504
 
1.8%
9 11178
 
1.3%
Other values (30) 38425
 
4.5%
Distinct597
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
2024-04-29T17:06:43.287623image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length7
Median length7
Mean length6.9307875
Min length4

Characters and Unicode

Total characters927.977
Distinct characters39
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique332 ?
Unique (%)0.2%

Sample

1st rowno_info
2nd rowno_info
3rd rowno_info
4th rowno_info
5th rowno_info
ValueCountFrequency (%)
no_info 130803
97.7%
i10 162
 
0.1%
f29 144
 
0.1%
f102 107
 
0.1%
f603 72
 
0.1%
r451 70
 
0.1%
f192 67
 
0.1%
f918 67
 
0.1%
f058 65
 
< 0.1%
x649 65
 
< 0.1%
Other values (587) 2270
 
1.7%
2024-04-29T17:06:45.357934image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 261606
28.2%
o 261606
28.2%
_ 130803
14.1%
f 130803
14.1%
i 130803
14.1%
0 1532
 
0.2%
F 1495
 
0.2%
1 1465
 
0.2%
9 1173
 
0.1%
2 1132
 
0.1%
Other values (29) 5559
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 927977
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 261606
28.2%
o 261606
28.2%
_ 130803
14.1%
f 130803
14.1%
i 130803
14.1%
0 1532
 
0.2%
F 1495
 
0.2%
1 1465
 
0.2%
9 1173
 
0.1%
2 1132
 
0.1%
Other values (29) 5559
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 927977
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 261606
28.2%
o 261606
28.2%
_ 130803
14.1%
f 130803
14.1%
i 130803
14.1%
0 1532
 
0.2%
F 1495
 
0.2%
1 1465
 
0.2%
9 1173
 
0.1%
2 1132
 
0.1%
Other values (29) 5559
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 927977
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 261606
28.2%
o 261606
28.2%
_ 130803
14.1%
f 130803
14.1%
i 130803
14.1%
0 1532
 
0.2%
F 1495
 
0.2%
1 1465
 
0.2%
9 1173
 
0.1%
2 1132
 
0.1%
Other values (29) 5559
 
0.6%

TPDISEC1
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
0
103360 
1
24627 
2
 
5905

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters133.892
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 103360
77.2%
1 24627
 
18.4%
2 5905
 
4.4%

Length

2024-04-29T17:06:45.793947image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-29T17:06:46.098943image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 103360
77.2%
1 24627
 
18.4%
2 5905
 
4.4%

Most occurring characters

ValueCountFrequency (%)
0 103360
77.2%
1 24627
 
18.4%
2 5905
 
4.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 133892
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 103360
77.2%
1 24627
 
18.4%
2 5905
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 133892
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 103360
77.2%
1 24627
 
18.4%
2 5905
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 133892
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 103360
77.2%
1 24627
 
18.4%
2 5905
 
4.4%

TPDISEC2
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
0
130803 
1
 
2444
2
 
645

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters133.892
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 130803
97.7%
1 2444
 
1.8%
2 645
 
0.5%

Length

2024-04-29T17:06:46.461961image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-29T17:06:46.831952image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 130803
97.7%
1 2444
 
1.8%
2 645
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 130803
97.7%
1 2444
 
1.8%
2 645
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 133892
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 130803
97.7%
1 2444
 
1.8%
2 645
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 133892
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 130803
97.7%
1 2444
 
1.8%
2 645
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 133892
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 130803
97.7%
1 2444
 
1.8%
2 645
 
0.5%

Interactions

2024-04-29T17:05:46.788084image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:01:58.300188image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:02:08.416919image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:02:18.451221image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:02:28.770330image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:02:39.094059image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:02:48.549525image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:03:01.476518image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:03:09.943763image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:03:18.983513image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:03:28.255495image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:03:37.208535image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:03:46.093633image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:03:54.507034image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:04:04.542272image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:04:13.381889image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:04:22.686755image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:04:31.987007image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:04:41.569119image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:04:50.828256image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:04:59.724944image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:05:09.049780image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:05:18.817784image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:05:28.139170image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:05:37.797365image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:05:47.109091image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:01:58.759179image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:02:08.791911image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:02:18.879630image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:02:29.118331image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:02:39.556941image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:02:48.969658image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:03:01.837894image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:03:10.296574image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:03:19.395095image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:03:28.570991image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:03:37.606655image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:03:46.354439image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:03:54.926244image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:04:04.859027image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:04:13.722779image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:04:23.116111image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:04:32.315581image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:04:41.951458image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:04:51.136461image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:05:00.051258image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:05:09.390430image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:05:19.242660image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:05:28.496171image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:05:38.140821image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:05:47.561235image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:01:59.169761image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:02:09.135515image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:02:19.323227image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:02:29.542127image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:02:39.966146image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:02:49.382251image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:03:02.144760image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:03:10.637113image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:03:19.752051image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:03:28.890981image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:03:37.947804image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:03:46.643129image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:03:55.213227image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:04:05.178532image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:04:14.125788image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:04:23.474816image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:04:32.667324image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:04:42.236362image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:04:51.465191image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:05:00.521552image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:05:09.750000image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:05:19.696146image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:05:28.882174image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:05:38.479851image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:05:47.927437image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:01:59.614053image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:02:09.503216image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:02:19.695227image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:02:29.976353image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:02:40.412401image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:02:49.728772image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:03:02.447576image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:03:11.067348image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:03:20.065040image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:03:29.239808image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:03:38.275102image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:03:46.971938image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:03:55.500230image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:04:05.520507image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:04:14.508286image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:04:23.896223image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:04:32.994457image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:04:42.656465image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:04:51.784022image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:05:00.826210image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:05:10.146942image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:05:20.077691image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:05:29.273520image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:05:38.809975image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:05:48.420255image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:02:00.020050image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:02:09.964937image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:02:20.233446image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-04-29T17:02:40.862043image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-04-29T17:03:02.788744image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-04-29T17:03:29.633265image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:03:38.663769image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:03:47.416028image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:03:55.834618image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:04:05.921515image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:04:14.828494image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:04:24.237022image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:04:33.385623image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:04:43.006156image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-04-29T17:05:01.134829image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-04-29T17:05:39.291498image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-04-29T17:02:41.352867image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-04-29T17:04:40.830973image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:04:49.989232image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:04:59.063899image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:05:08.324699image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:05:18.035807image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:05:27.458873image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:05:37.013888image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:05:46.010244image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:05:56.440067image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:02:08.025149image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:02:18.006552image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:02:28.360882image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:02:38.662705image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:02:48.185112image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:03:01.090280image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:03:09.583921image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:03:18.656030image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:03:27.849777image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:03:36.818710image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:03:45.807200image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:03:54.119719image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:04:04.089716image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:04:13.068285image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:04:22.316526image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:04:31.623399image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:04:41.162311image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:04:50.317786image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:04:59.352626image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:05:08.701448image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:05:18.412495image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:05:27.772104image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:05:37.351876image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-29T17:05:46.427629image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Missing values

2024-04-29T17:05:57.236367image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-29T17:05:59.708974image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

UF_ZIANO_CMPTMES_CMPTESPECCGC_HOSPN_AIHIDENTCEPMUNIC_RESNASCSEXODIAR_ACOMQT_DIARIASPROC_SOLICPROC_REAVAL_SHVAL_SPVAL_TOTUS_TOTDT_INTERDT_SAIDADIAG_PRINCCOBRANCANAT_JURGESTAOMUNIC_MOVIDADEDIAS_PERMCAR_INTGESTOR_TPGESTOR_CPFCNESCNPJ_MANTREGCTRACA_CORSEQUENCIAREMESSAAUD_JUSTDIAGSEC1DIAGSEC2TPDISEC1TPDISEC2
0350000201915463745000135043519101604414185703903523101970-01-01 00:00:00.01997120810230317014030317014099.5614.44114.0030.561970-01-01 00:00:00.0201901161970-01-01 00:00:00.020190118F19212102323523102122002078562463745000001947102362344HE35000001N201901.DTSNoneno_infono_info00
1350000201915463745000135043519101604766185765103523101970-01-01 00:00:00.019910417107303170140303170140348.4650.54399.00106.971970-01-01 00:00:00.0201901171970-01-01 00:00:00.020190124F19212102323523102772002078562463745000001947102262346HE35000001N201901.DTSNoneF209no_info10
2350000201915463745000135043519101604887185958503523101970-01-01 00:00:00.019620707309303170140303170140545.4664.98610.44163.651970-01-01 00:00:00.0201901171970-01-01 00:00:00.020190126F31212102323523105692002078562463745000001947102362347HE35000001N201901.DTSNoneF200no_info10
3350000201915463745000135043519101604953185955003523101970-01-01 00:00:00.0198209051013303170140303170140647.1493.86741.00198.651970-01-01 00:00:00.0201901171970-01-01 00:00:00.020190130F312121023235231036132002078562463745000001947102262348HE35000001N201901.DTSNoneF192no_info10
4350000201915463745000135043519101605195185700803523101970-01-01 00:00:00.019811220107303170140303170140348.4650.54399.00106.971970-01-01 00:00:00.0201901181970-01-01 00:00:00.020190125F31212102323523103772002078562463745000001947102162350HE35000001N201901.DTSNoneno_infono_info00
5350000201915463745000109043519101659249184523323550301970-01-01 00:00:00.019940831303303170131303170131149.3421.66171.0045.841970-01-01 00:00:00.0201901141970-01-01 00:00:00.020190117F32212102323550302432002079240463745000001947102363116HE35000001N201901.DTSNoneno_infono_info00
6350000201915463745000109043519101659623184611103550301970-01-01 00:00:00.019910815104303170131303170131199.1228.88228.0061.121970-01-01 00:00:00.0201901151970-01-01 00:00:00.020190119F19212102323550302742002079240463745000001947102163121HE35000001N201901.DTSNoneF329no_info20
73500002019154637450000493935111046349725140620773543401970-01-01 00:00:00.01974071710313031700933031700931052.51143.841196.35320.731970-01-01 00:00:00.0201102131970-01-01 00:00:00.020190131F192231023235434044312002078031463745000001947102216156HE35000001N201901.DTSNoneno_infono_info00
83500002019154637450000493935181323503705140932003543401970-01-01 00:00:00.0199310141020303170093303170093679.0492.80771.84206.921970-01-01 00:00:00.0201812031970-01-01 00:00:00.020190121F1921210232354340252120020780314637450000019471029916264HE35000001N201901.DTSNoneno_infono_info00
93500002019154637450000493935121155991805133600003510401970-01-01 00:00:00.01981062510313031700933031700931052.51143.841196.35320.731970-01-01 00:00:00.0201208061970-01-01 00:00:00.020190131F190211023235434037312002078031463745000001947102316162HE35000001N201901.DTSNoneno_infono_info00
UF_ZIANO_CMPTMES_CMPTESPECCGC_HOSPN_AIHIDENTCEPMUNIC_RESNASCSEXODIAR_ACOMQT_DIARIASPROC_SOLICPROC_REAVAL_SHVAL_SPVAL_TOTUS_TOTDT_INTERDT_SAIDADIAG_PRINCCOBRANCANAT_JURGESTAOMUNIC_MOVIDADEDIAS_PERMCAR_INTGESTOR_TPGESTOR_CPFCNESCNPJ_MANTREGCTRACA_CORSEQUENCIAREMESSAAUD_JUSTDIAGSEC1DIAGSEC2TPDISEC1TPDISEC2
1338823500002023125463745000138493523124217646166652053522501970-01-01 00:00:00.0196203093014303170140303170140696.92101.08798.00161.211970-01-01 00:00:00.0202312131970-01-01 00:00:00.020231227F319121023235225061142002078104463745000001947102362176HE35000001N202312.DTSnanF059I1011
1338833500002023125463745000138493523124205370164220403505701970-01-01 00:00:00.019880225105303170182303170182248.9036.10285.0057.571970-01-01 00:00:00.0202311281970-01-01 00:00:00.020231203F19212102323522503552002078104463745000001947102362169HE35000001N202312.DTSnanF102no_info10
1338843500002023125463745000138493523124209550166630003522501970-01-01 00:00:00.0200404271015303170182303170182941.58108.301049.88212.091970-01-01 00:00:00.0202311271970-01-01 00:00:00.020231212F195121023235225019152002078104463745000001947102162173HE35000001N202312.DTSnanF121no_info10
1338853500002023125463745000132533523101950203129380003550301970-01-01 00:00:00.0199001011019303170093303170093830.89113.43944.32190.771970-01-01 00:00:00.0202310291970-01-01 00:00:00.020231117F1921210232355030331911120225941342077418463745000001947102345084HE35000001N202312.DTSnanno_infono_info00
1338863500002023125463745000132533523101950225129380003550301970-01-01 00:00:00.019970101104303170093303170093174.9223.88198.8040.161970-01-01 00:00:00.0202310281970-01-01 00:00:00.020231101F192141023235503026411120225941342077418463745000001947102145085HE35000001N202312.DTSnanno_infono_info00
1338873500002023125463745000132533523101950313129380003550301970-01-01 00:00:00.01983010110230317009330317009387.4611.9499.4020.081970-01-01 00:00:00.0202311041970-01-01 00:00:00.020231106F192141023235503040211120225941342077418463745000001947102145089HE35000001N202312.DTSnanno_infono_info00
1338883500002023123035231030566931131654963503801970-01-01 00:00:00.01994020130230317014030317014049.787.2257.0011.511970-01-01 00:00:00.0202310221970-01-01 00:00:00.020231024F3125111122350950292200207979846068425000133710239866HE35000001N202312.DTSnanno_infono_info00
1338893500002023123035231031412501133877643533401970-01-01 00:00:00.01975092530213031701403031701401050.38151.621202.00242.821970-01-01 00:00:00.0202309201970-01-01 00:00:00.020231011F319111112235095047211002079798460684250001337102310198HE35000001N202312.DTSnanno_infono_info00
1338903500002023123035231031439341130607433509501970-01-01 00:00:00.01998071430303031701403031701401499.40216.601716.00346.661970-01-01 00:00:00.0202309021970-01-01 00:00:00.020231002F311181112235095025302173772248782079798460684250001337102110423HE35000001N202312.DTSnanno_infono_info00
1338913500002023123035231031439781130607433509501970-01-01 00:00:00.0199807143015303170140303170140746.70108.30855.00172.721970-01-01 00:00:00.0202310021970-01-01 00:00:00.020231017F311121112235095025152173772248782079798460684250001337102110427HE35000001N202312.DTSnanno_infono_info00